Analysis for SSFA project: NewEplsar model

Filtered strongly by < 20% NMP

Table of contents

1. Used packages
2. Global settings
3. Load data
4. Explore data
5. Model specification
6. Inference
   1. NewEplsar
7. Summary

Used packages

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import pickle
import arviz as az
import pymc3 as pm

from matplotlib.colors import to_rgb

import scipy.stats as stats 

from IPython.display import display

import matplotlib as mpl

%load_ext autoreload
%autoreload 2

import plotting_lib

Global settings

Output

writeOut = True
outPathPlots = "../plots/statistical_model_neweplsar_filter_weak/"
outPathData = "../derived_data/statistical_model_neweplsar_filter_weak/"
prefix = "NewEplsar_filter_weak"

Plotting

widthMM = 190 
widthInch = widthMM / 25.4
ratio = 0.66666
heigthInch = ratio*widthInch

SMALL_SIZE = 8
MEDIUM_SIZE = 10
BIGGER_SIZE = 12

plt.rc('font', size=SMALL_SIZE)          # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title
sns.set_style("ticks")

dpi = 300

sizes = [SMALL_SIZE,MEDIUM_SIZE,BIGGER_SIZE]

Computing

numSamples = 1000
numCores = 4
numTune = 1000
numPredSamples = 2000
random_seed=3651456135

Load data

datafile = "../derived_data/preprocessing/preprocessed_filter_weak.dat"

with open(datafile, "rb") as f:
    x1,x2,_,df,dataZ,dictMeanStd,dictTreatment,dictSoftware = pickle.load(f)    

Show that everything is correct:

display(pd.DataFrame.from_dict({'x1':x1,'x2':x2}))

	x1	x2
0	0	5
1	1	5
2	0	5
3	1	5
4	0	5
...	...	...
273	1	9
274	0	9
275	1	9
276	0	9
277	1	9

278 rows × 2 columns

x1 indicates the software used, x2 indicates the treatment applied.

for surfaceParam,(mean,std) in dictMeanStd.items():
    print("Surface parameter {} has mean {} and standard deviation {}".format(surfaceParam,mean,std))

Surface parameter epLsar has mean 0.0032388209205305753 and standard deviation 0.0019378273835719989
Surface parameter Rsquared has mean 0.9974096825435252 and standard deviation 0.007283582118542012
Surface parameter Asfc has mean 14.919474245449283 and standard deviation 12.47068676838922
Surface parameter Smfc has mean 1.155270960424856 and standard deviation 7.13503174525663
Surface parameter HAsfc9 has mean 0.44593694325514915 and standard deviation 0.7912033512620836
Surface parameter HAsfc81 has mean 0.9300206156734742 and standard deviation 2.3638534390774013

for k,v in sorted(dictTreatment.items(), key=lambda x: x[0]):    
    print("Number {} encodes treatment {}".format(k,v))

Number 0 encodes treatment BrushDirt
Number 1 encodes treatment BrushNoDirt
Number 2 encodes treatment Clover
Number 3 encodes treatment Clover+dust
Number 4 encodes treatment Control
Number 5 encodes treatment Dry bamboo
Number 6 encodes treatment Dry grass
Number 7 encodes treatment Dry lucerne
Number 8 encodes treatment Grass
Number 9 encodes treatment Grass+dust
Number 10 encodes treatment RubDirt

for k,v in dictSoftware.items():
    print("Number {} encodes software {}".format(k,v))

Number 0 encodes software ConfoMap
Number 1 encodes software Toothfrax

display(dataZ)

	index	TreatmentNumber	SoftwareNumber	DatasetNumber	NameNumber	epLsar_z	Rsquared_z	Asfc_z	Smfc_z	HAsfc9_z	HAsfc81_z
0	0	5	0	0	115	0.608031	0.081494	-0.261684	-0.120632	-0.391977	-0.239736
1	1	5	1	0	115	0.764866	0.295228	-0.368764	-0.145206	-0.392397	-0.240365
2	2	5	0	0	116	1.355641	-0.166422	0.043912	-0.120632	-0.346351	-0.268091
3	3	5	1	0	116	1.350574	0.282460	-0.137943	-0.145206	-0.349727	-0.282929
4	4	5	0	0	117	0.930308	-0.359987	-0.137793	-0.120632	-0.233444	-0.221925
...	...	...	...	...	...	...	...	...	...	...	...
273	273	9	1	2	52	0.611602	0.267769	-1.050437	0.035516	0.493708	0.076860
274	274	9	0	2	53	0.084569	0.197735	-0.966638	-0.093723	0.242115	0.257597
275	275	9	1	2	53	-0.051512	0.319804	-0.975181	-0.143224	0.644288	0.381453
276	276	9	0	2	54	-1.041990	0.284041	-1.077552	0.011489	-0.095103	-0.053253
277	277	9	1	2	54	-1.308590	0.336005	-1.081522	-0.104672	-0.126169	-0.038105

278 rows × 11 columns

display(df)

	Dataset	Name	Software	Diet	Treatment	Before.after	NMP	NMP_cat	epLsar	Rsquared	Asfc	Smfc	HAsfc9	HAsfc81	NewEplsar	TreatmentNumber	SoftwareNumber	DatasetNumber	NameNumber
0	GuineaPigs	capor_2CC6B1_txP4_#1_1_100xL_1	ConfoMap	Dry bamboo	Dry bamboo	NaN	0.717312	0-5%	0.004417	0.998003	11.656095	0.294557	0.135803	0.363319	0.019460	5	0	0	115
1	GuineaPigs	capor_2CC6B1_txP4_#1_1_100xL_1	Toothfrax	Dry bamboo	Dry bamboo	NaN	0.717312	0-5%	0.004721	0.999560	10.320730	0.119219	0.135471	0.361833	NaN	5	1	0	115
2	GuineaPigs	capor_2CC6B1_txP4_#1_1_100xL_2	ConfoMap	Dry bamboo	Dry bamboo	NaN	1.674215	0-5%	0.005866	0.996198	15.467083	0.294557	0.171903	0.296292	0.020079	5	0	0	116
3	GuineaPigs	capor_2CC6B1_txP4_#1_1_100xL_2	Toothfrax	Dry bamboo	Dry bamboo	NaN	1.674215	0-5%	0.005856	0.999467	13.199232	0.119219	0.169232	0.261217	NaN	5	1	0	116
4	GuineaPigs	capor_2CC6B1_txP4_#1_1_100xL_3	ConfoMap	Dry bamboo	Dry bamboo	NaN	1.760409	0-5%	0.005042	0.994788	13.201101	0.294557	0.261235	0.405422	0.019722	5	0	0	117
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
273	Sheeps	L8-Ovis-90730-lm2sin-a	Toothfrax	Grass+dust	Grass+dust	NaN	0.000000	0-5%	0.004424	0.999360	1.819802	1.408678	0.836560	1.111706	NaN	9	1	2	52
274	Sheeps	L8-Ovis-90764-lm2sin-a	ConfoMap	Grass+dust	Grass+dust	NaN	0.000000	0-5%	0.003403	0.998850	2.864831	0.486556	0.637499	1.538943	0.018978	9	0	2	53
275	Sheeps	L8-Ovis-90764-lm2sin-a	Toothfrax	Grass+dust	Grass+dust	NaN	0.000000	0-5%	0.003139	0.999739	2.758297	0.133366	0.955699	1.831721	NaN	9	1	2	53
276	Sheeps	L8-Ovis-90814-lm2sin-a	ConfoMap	Grass+dust	Grass+dust	NaN	0.000000	0-5%	0.001220	0.999479	1.481662	1.237247	0.370691	0.804138	0.017498	9	0	2	54
277	Sheeps	L8-Ovis-90814-lm2sin-a	Toothfrax	Grass+dust	Grass+dust	NaN	0.000000	0-5%	0.000703	0.999857	1.432148	0.408433	0.346111	0.839946	NaN	9	1	2	54

278 rows × 19 columns

Exploration

dfNewAvail = df[~df.NewEplsar.isna()].copy()

We look at the overall relationship between both epLsar variants on ConfoMap.

yrange = [0.015,0.022]
xrange = [ -0.0005 , 0.0085]

ax = sns.scatterplot(data=dfNewAvail,x='epLsar',y='NewEplsar');
ax.set_xlim(xrange);
ax.set_ylim(yrange);

Could be linear, but there is also a lot of noise.

Maybe different treatments have different behavior?

ax = sns.scatterplot(data=dfNewAvail,x='epLsar',y='NewEplsar',hue="Treatment");
ax.set_xlim(xrange);
ax.set_ylim(yrange);

Too crowded, let's try it per dataset

ax = sns.scatterplot(data=dfNewAvail[dfNewAvail.Dataset == "Sheeps"],x='epLsar',y='NewEplsar',hue="Treatment");
ax.set_xlim(xrange);
ax.set_ylim(yrange);

ax = sns.scatterplot(data=dfNewAvail[dfNewAvail.Dataset == "GuineaPigs"],x='epLsar',y='NewEplsar',hue="Treatment");
ax.set_xlim(xrange);
ax.set_ylim(yrange);

ax = sns.scatterplot(data=dfNewAvail[dfNewAvail.Dataset == "Lithics"],x='epLsar',y='NewEplsar',hue="Treatment");
ax.set_xlim(xrange);
ax.set_ylim(yrange);

Standardization in z scores

dfNewAvail.NewEplsar

0      0.019460
2      0.020079
4      0.019722
6      0.020694
8      0.019841
         ...   
268    0.018765
270    0.018581
272    0.017697
274    0.018978
276    0.017498
Name: NewEplsar, Length: 139, dtype: float64

mu = dfNewAvail.NewEplsar.mean()
mu

0.018327855160863306

sig = dfNewAvail.NewEplsar.std()
sig

0.0010227435286363504

dictMeanStd['NewEplsar'] = (mu,sig)

newZ = (dfNewAvail.NewEplsar.values-mu) / sig

newIndex = df[~df.NewEplsar.isna()].index.values

Model specification

class Model_NewEplsar(pm.Model):
    
    """
    Compute params of priors and hyperpriors.
    """
    def getParams(self,x2,y):
        # get lengths        
        Nx2Lvl = np.unique(x2).size
        
        dims = (Nx2Lvl)
        
        ### get standard deviations
        
        # convert to pandas dataframe to use their logic
        df = pd.DataFrame.from_dict({'x2':x2,'y':y})
        
        s2 = df.groupby('x2').std()['y'].max()
        stdSingle = s2
        
        return (dims, stdSingle)
    
    def printParams(self,x2,y):
        dims, stdSingle= self.getParams(x2,y)
        Nx2Lvl = dims
        s2 = stdSingle
                
        print("The number of levels of the x variables are {}".format(dims))
        print("The standard deviations used for the beta prior is {}".format(stdSingle))        
    
    def __init__(self,name,x2,y,model=None):
        
        # call super's init first, passing model and name
        super().__init__(name, model)
        
        # get parameter of hyperpriors
        dims, stdSingle = self.getParams(x2,y)
        Nx2Lvl  = dims
        s2 = stdSingle
                
        ### hyperpriors ### 
        # observation hyperpriors
        lamY = 1/30.
        muGamma = 0.5
        sigmaGamma = 2.
        
        # prediction hyperpriors
        sigma0 = pm.HalfNormal('sigma0',sd=1)         
        sigma2 = pm.HalfNormal('sigma2',sd=s2, shape=Nx2Lvl)
        
        
        mu_b0 = pm.Normal('mu_b0', mu=0., sd=1)              
        mu_b2 = pm.Normal('mu_b2', mu=0., sd=1, shape=Nx2Lvl)       
        beta2 = 1.5*(np.sqrt(6)*sigma2)/(np.pi)
                                       
        ### priors ### 
        # observation priors        
        nuY = pm.Exponential('nuY',lam=lamY)
        sigmaY = pm.Gamma('sigmaY',mu=muGamma, sigma=sigmaGamma)
        
        # prediction priors
        b0_dist = pm.Normal('b0_dist', mu=0, sd=1)
        b0 = pm.Deterministic("b0", mu_b0 + b0_dist * sigma0)
                        
        b2_beta = pm.HalfNormal('b2_beta', sd=beta2, shape=Nx2Lvl)
        b2_dist = pm.Gumbel('b2_dist', mu=0, beta=1)
        b2 = pm.Deterministic("b2", mu_b2 + b2_beta * b2_dist)
        
        #### prediction ###         
        mu = pm.Deterministic('mu',b0 + b2[x2])
                                        
        ### observation ### 
        y = pm.StudentT('y',nu = nuY, mu=mu, sd=sigmaY, observed=y)

Inference

NewEplsar

with pm.Model() as model:
    new_epLsarModel = Model_NewEplsar('NewEplsar',x2[newIndex],newZ)

Verify model settings

new_epLsarModel.printParams(x2[newIndex],newZ)

The number of levels of the x variables are 11
The standard deviations used for the beta prior is 1.1366670418374532

try:
    graph_new_epLsar = pm.model_to_graphviz(new_epLsarModel)    
except:
    graph_new_epLsar = "Could not make graph"
graph_new_epLsar

Check prior choice

with new_epLsarModel as model:
    prior_pred_new_epLsar = pm.sample_prior_predictive(samples=numPredSamples,random_seed=random_seed)

plotting_lib.plotPriorPredictive(widthInch,heigthInch,dpi,writeOut,outPathPlots,dfNewAvail.reset_index(),\
                                 dictMeanStd,prior_pred_new_epLsar,newZ,'NewEplsar',prefix)

Prior choice is as intended: Broad over the data range.

Sampling

with new_epLsarModel as model:
    trace_new_epLsar = pm.sample(numSamples,cores=numCores,tune=numTune,max_treedepth=20, init='auto',target_accept=0.99,random_seed=random_seed)

Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [NewEplsar_b2_dist, NewEplsar_b2_beta, NewEplsar_b0_dist, NewEplsar_sigmaY, NewEplsar_nuY, NewEplsar_mu_b2, NewEplsar_mu_b0, NewEplsar_sigma2, NewEplsar_sigma0]

100.00% [8000/8000 01:05<00:00 Sampling 4 chains, 0 divergences]

Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.
The number of effective samples is smaller than 10% for some parameters.

with new_epLsarModel as model:
    if writeOut:
        with open(outPathData + '{}_model_{}.pkl'.format(prefix,'NewEplsar'), 'wb') as buff:
            pickle.dump({'model':new_epLsarModel, 'trace': trace_new_epLsar}, buff)

Check sampling

with new_epLsarModel as model:
    dataTrace_new_epLsar = az.from_pymc3(trace=trace_new_epLsar)

pm.summary(dataTrace_new_epLsar,hdi_prob=0.95).round(2)

	mean	sd	hdi_2.5%	hdi_97.5%	mcse_mean	mcse_sd	ess_mean	ess_sd	ess_bulk	ess_tail	r_hat
NewEplsar_mu_b0	-0.25	0.64	-1.45	1.09	0.02	0.01	1816.0	1816.0	1794.0	2528.0	1.0
NewEplsar_mu_b2[0]	-0.77	0.49	-1.72	0.16	0.01	0.01	1994.0	1994.0	2034.0	2364.0	1.0
NewEplsar_mu_b2[1]	-0.36	0.46	-1.31	0.48	0.01	0.01	1787.0	1787.0	1786.0	2348.0	1.0
NewEplsar_mu_b2[2]	0.13	0.46	-0.79	1.00	0.01	0.01	1830.0	1830.0	1814.0	2326.0	1.0
NewEplsar_mu_b2[3]	-0.19	0.45	-1.13	0.68	0.01	0.01	1674.0	1674.0	1687.0	2009.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...
NewEplsar_mu[134]	-0.59	0.22	-1.02	-0.16	0.00	0.00	4438.0	4264.0	4439.0	3252.0	1.0
NewEplsar_mu[135]	-0.59	0.22	-1.02	-0.16	0.00	0.00	4438.0	4264.0	4439.0	3252.0	1.0
NewEplsar_mu[136]	-0.59	0.22	-1.02	-0.16	0.00	0.00	4438.0	4264.0	4439.0	3252.0	1.0
NewEplsar_mu[137]	-0.59	0.22	-1.02	-0.16	0.00	0.00	4438.0	4264.0	4439.0	3252.0	1.0
NewEplsar_mu[138]	-0.59	0.22	-1.02	-0.16	0.00	0.00	4438.0	4264.0	4439.0	3252.0	1.0

190 rows × 11 columns

az.plot_forest(dataTrace_new_epLsar,var_names=['b0','b2'],filter_vars='like',figsize=(widthInch,5*heigthInch),hdi_prob=0.95,ess=True,r_hat=True);
if writeOut:
    plt.savefig(outPathPlots + "{}_posterior_forest_{}.pdf".format(prefix,'NewEplsar'),dpi=dpi)

with new_epLsarModel as model:
    plotting_lib.plotTracesB(widthInch,heigthInch,dpi,writeOut,outPathPlots,trace_new_epLsar,'NewEplsar',prefix)

with new_epLsarModel as model:
    plotting_lib.pm.energyplot(trace_new_epLsar)

Posterior predictive distribution

with new_epLsarModel as model:
    posterior_pred_new_epLsar = pm.sample_posterior_predictive(trace_new_epLsar,samples=numPredSamples,random_seed=random_seed)

/home/bob/Documents/Projekt_Neuwied/SSFA/ssfa-env/lib/python3.7/site-packages/pymc3/sampling.py:1708: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample
  "samples parameter is smaller than nchains times ndraws, some draws "

100.00% [2000/2000 00:01<00:00]

plotting_lib.plotPriorPosteriorPredictive(widthInch,heigthInch,dpi,writeOut,\
                                          outPathPlots,dfNewAvail.reset_index(),dictMeanStd,\
                                          prior_pred_new_epLsar,posterior_pred_new_epLsar,newZ,'NewEplsar',prefix)

Posterior check

with new_epLsarModel as model:
    pm_data_new_epLsar = az.from_pymc3(trace=trace_new_epLsar,prior=prior_pred_new_epLsar,posterior_predictive=posterior_pred_new_epLsar)

arviz.data.io_pymc3 - WARNING - posterior predictive variable NewEplsar_y's shape not compatible with number of chains and draws. This can mean that some draws or even whole chains are not represented.

plotting_lib.plotPosterior(widthInch,heigthInch,dpi,writeOut,outPathPlots,dictMeanStd,\
                           pm_data_new_epLsar,'NewEplsar',prefix)

Compare treatment differences with other epLsar values

b1P_Old = np.load("../derived_data/statistical_model_two_factors_filter_weak/statistical_model_two_factors_filter_weak_epLsar_oldb1.npy")
b2P_Old = np.load("../derived_data/statistical_model_two_factors_filter_weak/statistical_model_two_factors_filter_weak_epLsar_oldb2.npy")
M12P_Old = np.load("../derived_data/statistical_model_two_factors_filter_weak/statistical_model_two_factors_filter_weak_epLsar_oldM12.npy")

from collections import defaultdict 

def plotTreatmentPosterior(widthInch,heigthInch,dpi,sizes,writeOut,path,dictMeanStd,dictTreatment,dictSoftware,trace,yname,x1,x3,prefix):
        
    SMALL_SIZE,MEDIUM_SIZE,BIGGER_SIZE = sizes
    
    mu_Val,sig_Val = dictMeanStd[yname]
    
    # get posterior samples    
    b2P = sig_Val*trace['{}_b2'.format(yname)]
    
    # prepare color dict for treatments
    # use groups of 4 colors, as in tab20c
    colorIndex = dict({5:0,6:1,7:2,0:4,1:5,4:6,10:7,2:8,3:9,8:10,9:11})
    
    # prepare dataset dict for treatments   
    dictDataset = dict({5:0,6:0,7:0,0:1,1:1,4:1,10:1,2:2,3:2,8:2,9:2})
    
    # === inverse dict ==== 
    inv_dictDataset = defaultdict(list)                                                                  
  
    # using loop to perform reverse mapping 
    for keys, vals in dictDataset.items():  
        for val in [vals]:  
            inv_dictDataset[val].append(keys) 
    # === 
    
    # get number of datasets    
    numDatasets = len(np.unique(list(dictDataset.values())))
    
    # get number of treatments per dataset
    dictDataset2NumberTreats = dict()
    for numDataset in range(numDatasets):        
        n = len(inv_dictDataset[numDataset])
        dictDataset2NumberTreats[numDataset] = n      
         
    # Get maximum of treatments per dataset
    tmax = np.max(list(dictDataset2NumberTreats.values()))
    
    
    # compute maximal number of pairs 
    maxpair = int(tmax*(tmax-1)/2)
    
    
    fig = plt.subplots(squeeze=False, figsize=(numDatasets*widthInch,maxpair*heigthInch), dpi=dpi);
    
    # store list for hdi
    hdiList = []

    for indexDataset in np.arange(numDatasets):
        # counter for row
        rowCounter = 0
        
        # first treatment 
        for treatmentNum_i,lvl2_i in enumerate(inv_dictDataset[indexDataset]):
            
            # second treatment 
            for treatmentNum_j,lvl2_j in enumerate(inv_dictDataset[indexDataset]):
                
                if treatmentNum_i > treatmentNum_j:
                                       
                    
                    # set subplot                    
                    curr_ax = plt.subplot2grid((maxpair, numDatasets), (rowCounter,indexDataset))
  
                    # compute difference between treatments for each software
                    diffS0 = sig_Val*((M12P_Old[:,0,lvl2_i]+b2P_Old[:,lvl2_i]) -(M12P_Old[:,0,lvl2_j]+b2P_Old[:,lvl2_j]))
                    diffS1 = sig_Val*((M12P_Old[:,1,lvl2_i]+b2P_Old[:,lvl2_i]) -(M12P_Old[:,1,lvl2_j]+b2P_Old[:,lvl2_j]))
                    
                    #plot posterior                    
                    sns.kdeplot(diffS1,ax=curr_ax,label="epLsar on {}".format(dictSoftware[1]),color='gray',alpha=0.3,ls='--');
                    sns.kdeplot(diffS0,ax=curr_ax,label="epLsar on {}".format(dictSoftware[0]),color='gray',alpha=0.3,ls='dotted');                    
                    sns.kdeplot(b2P[:,lvl2_i]-b2P[:,lvl2_j],ax=curr_ax,label="NewEplsar on {}".format(dictSoftware[0]),color='C0',alpha=0.3,ls='dotted');
                                        
                    # plot reference value zero
                    curr_ax.axvline(x=0,color="C1")
                    
                    # get hdi
                    hdi_new = az.hdi(az.convert_to_inference_data(b2P[:,lvl2_i]-b2P[:,lvl2_j]),hdi_prob=0.95)['x'].values
                    hdiS0 = az.hdi(az.convert_to_inference_data(diffS0),hdi_prob=0.95)['x'].values
                    hdiS1 = az.hdi(az.convert_to_inference_data(diffS1),hdi_prob=0.95)['x'].values  
                    
                    isSignificant = lambda x: (x[0] > 0.0) or (x[1] < 0.0)
                    
                    # store hdi
                    hdiList.append([dictTreatment[lvl2_i],dictTreatment[lvl2_j],
                                    hdi_new[0],hdi_new[1],isSignificant(hdi_new),                                 
                                   hdiS0[0],hdiS0[1],isSignificant(hdiS0),
                                    hdiS1[0],hdiS1[1],isSignificant(hdiS1)
                                   ])
                    
                    # set title 
                    nameFirst = dictTreatment[lvl2_i]
                    nameSecond = dictTreatment[lvl2_j]
                    title = "{} vs. {}".format(nameFirst,nameSecond)
                    if isSignificant(hdi_new):
                        title += ": Significant on NewEplsar"

                            
                    curr_ax.set_title(title)
                    
                    # add legend
                    curr_ax.legend()   
                    
                    # set x label
                    curr_ax.set_xlabel('Delta')
                    
                    # remove y label decoration
                    curr_ax.tick_params(left=False)
                    curr_ax.set(yticklabels=[])
                    
                    
                    # increment counter
                    rowCounter += 1
                    
    #plt.suptitle('Estimated differences between treatments on {}'.format(yname))
    
    plt.tight_layout()                
    
    if writeOut:
        plt.savefig(path + "treatment_pairs_{}.pdf".format(yname),dpi=dpi)
    
    plt.show()
    
    # convert hdi to df
    df = pd.DataFrame(hdiList,columns=["Treatment_i","Treatment_j",
                                       "hdi_NewEplsar_2.5%","hdi_NewEplsar_97.5%","isSignificant_NewEplsar","hdi_{}_2.5%".format(dictSoftware[0]),"hdi_{}_97.5%".format(dictSoftware[0]),"isSignificant_on_{}".format(dictSoftware[0]),
                                  "hdi_{}_2.5%".format(dictSoftware[1]),"hdi_{}_97.5%".format(dictSoftware[1]),"isSignificant_on_{}".format(dictSoftware[1])])
    return df

dfHDI = plotTreatmentPosterior(widthInch,heigthInch,dpi,sizes,writeOut,outPathPlots,dictMeanStd,dictTreatment,dictSoftware,trace_new_epLsar,'NewEplsar',x1[newIndex],x2[newIndex],prefix)

dfHDI

	Treatment_i	Treatment_j	hdi_NewEplsar_2.5%	hdi_NewEplsar_97.5%	isSignificant_NewEplsar	hdi_ConfoMap_2.5%	hdi_ConfoMap_97.5%	isSignificant_on_ConfoMap	hdi_Toothfrax_2.5%	hdi_Toothfrax_97.5%	isSignificant_on_Toothfrax
0	Dry grass	Dry bamboo	-0.002178	-0.001432	True	-0.002191	-0.001369	True	-0.002109	-0.001265	True
1	Dry lucerne	Dry bamboo	-0.001583	-0.000877	True	-0.001755	-0.000944	True	-0.001852	-0.001067	True
2	Dry lucerne	Dry grass	0.000157	0.000910	True	0.000030	0.000829	True	-0.000170	0.000652	False
3	BrushNoDirt	BrushDirt	-0.000292	0.001164	False	-0.000727	0.001097	False	-0.000948	0.000677	False
4	Control	BrushDirt	0.000253	0.001714	True	-0.000555	0.001088	False	-0.000342	0.001413	False
5	Control	BrushNoDirt	-0.000210	0.001274	False	-0.000899	0.001002	False	-0.000316	0.001475	False
6	RubDirt	BrushDirt	-0.000763	0.000687	False	-0.000378	0.001560	False	-0.000888	0.000746	False
7	RubDirt	BrushNoDirt	-0.001225	0.000242	False	-0.000735	0.001439	False	-0.000700	0.000890	False
8	RubDirt	Control	-0.001713	-0.000264	True	-0.000707	0.001324	False	-0.001397	0.000348	False
9	Clover+dust	Clover	-0.000923	0.000248	False	-0.000593	0.000735	False	-0.000486	0.000896	False
10	Grass	Clover	-0.000696	0.000588	False	0.000027	0.001602	True	0.000204	0.001960	True
11	Grass	Clover+dust	-0.000310	0.000979	False	-0.000121	0.001507	False	0.000026	0.001754	True
12	Grass+dust	Clover	-0.001042	0.000196	False	0.000175	0.001740	True	0.000327	0.001812	True
13	Grass+dust	Clover+dust	-0.000685	0.000490	False	0.000088	0.001711	True	0.000150	0.001657	True
14	Grass+dust	Grass	-0.001022	0.000228	False	-0.000754	0.001056	False	-0.000896	0.000879	False

if writeOut:
    dfHDI.to_csv(outPathData+ '{}_hdi_{}_filter_weak.csv'.format(prefix,'NewEplsar'))

Summary

Show where NewEplsar yields other results then epLsar:

df_summary = dfHDI[(dfHDI.isSignificant_NewEplsar != dfHDI.isSignificant_on_ConfoMap) | (dfHDI.isSignificant_NewEplsar != dfHDI.isSignificant_on_Toothfrax) ][["Treatment_i","Treatment_j","isSignificant_NewEplsar","isSignificant_on_Toothfrax","isSignificant_on_ConfoMap","hdi_NewEplsar_2.5%","hdi_NewEplsar_97.5%","hdi_ConfoMap_2.5%","hdi_ConfoMap_97.5%","hdi_Toothfrax_2.5%","hdi_Toothfrax_97.5%"]]
df_summary

	Treatment_i	Treatment_j	isSignificant_NewEplsar	isSignificant_on_Toothfrax	isSignificant_on_ConfoMap	hdi_NewEplsar_2.5%	hdi_NewEplsar_97.5%	hdi_ConfoMap_2.5%	hdi_ConfoMap_97.5%	hdi_Toothfrax_2.5%	hdi_Toothfrax_97.5%
2	Dry lucerne	Dry grass	True	False	True	0.000157	0.000910	0.000030	0.000829	-0.000170	0.000652
4	Control	BrushDirt	True	False	False	0.000253	0.001714	-0.000555	0.001088	-0.000342	0.001413
8	RubDirt	Control	True	False	False	-0.001713	-0.000264	-0.000707	0.001324	-0.001397	0.000348
10	Grass	Clover	False	True	True	-0.000696	0.000588	0.000027	0.001602	0.000204	0.001960
11	Grass	Clover+dust	False	True	False	-0.000310	0.000979	-0.000121	0.001507	0.000026	0.001754
12	Grass+dust	Clover	False	True	True	-0.001042	0.000196	0.000175	0.001740	0.000327	0.001812
13	Grass+dust	Clover+dust	False	True	True	-0.000685	0.000490	0.000088	0.001711	0.000150	0.001657

if writeOut:
    df_summary.to_csv(outPathData+ 'neweplsar_summary_filter_weak.csv')

Write out

!jupyter nbconvert --to html Statistical_Model_NewEplsar_filter_weak.ipynb

!jupyter nbconvert --to markdown Statistical_Model_NewEplsar_filter_weak.ipynb